Researchers at the Indian Institute of Technology (IIT) Mandi, working together with their counterparts at the Central Potato Research Institute (CPRI) in Shimla, have developed an AI solution that can detect diseases of the tuber crop simply by examining photographs of potato leaves.

This computational model for automated disease detection in potato crops was developed by the IIT Mandi researchers led by Srikant Srinivasan, an associate professor at the School of Computing and Electrical Engineering, and CPRI colleagues under a project funded by the Department of Biotechnology, a statement said on Monday.

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The blight is a common disease of the potato plant, that starts as uneven light green lesions near the tip and the margins of the leaf and then spreads into large brown to purplish-black necrotic patches that eventually leads to rotting of the plant. If left undetected and unchecked, blight could destroy the entire crop within a week under conducive conditions.

The computational tool developed by the IIT Mandi scientists, on the other hand, can detect blight in potato leaf images. The model is built using an AI tool called mask region-based convolutional neural network architecture and can accurately highlight the diseased portions of the leaf amid a complex background of plant and soil matter.

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“In India, as with most developing countries, the detection and identification of blight are performed manually by trained personnel who scout the field and visually inspect potato foliage,” explained Srinivasan . This process, as expected, is tedious and often impractical, especially for remote areas, because it requires the expertise of a horticultural specialist who may not be physically accessible.

“Automated disease detection can help in this regard and given the extensive proliferation of the mobile phones across the country, the smartphone could be a useful tool in this regard,” said Joe Johnson, IIT Mandi research scholar while highlighting potential application of this research.

The advanced HD cameras, better computing power and communication avenues offered by smartphones offer a promising platform for automated disease detection in crops.

To develop a robust model, healthy and diseased leaf data were collected from fields across Punjab, Uttar Pradesh and Himachal Pradesh, the scientists said.

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“Analysis of the detection performance indicates an overall precision of 98 per cent on leaf images in field environments,” said Srinivasan.

Following this success, the team is sizing down the model to a few tens of megabytes so that it can be hosted on a smartphone as an application. With this, when the farmer will photograph the leaf which appears unhealthy, the application will confirm in real-time if the leaf is infected or not.

With this timely knowledge, the farmer would know exactly when to spray the field, saving his produce and minimising costs associated with unnecessary use of fungicides.

Apart from Srinivasan and Johnson, IIT Mandi faculty Shyam K Masakapalli, research student Geetanjali Sharma and their CPRI counterparts Vijay Kumar Dua, Sanjeev Sharma and Jagdev Sharma participated in the research recently published in the journal ‘Plant Phenomics’.